class_docstr: Blocked Time Series cross-validator
  
  This cross-validation object is a modification of TimeSeriesSplit.
  In the kth split, it splits kth fold to train and test by time.
  
  Parameters
  ----------
  n_splits : int, default=5
      Number of splits. Must be at least 2.
  
  test_size : float, default=0.2
      Should be between 0.0 and 1.0 and represent the proportion
      of groups to include in the test split
docstr: Returns the number of splitting iterations in the cross-validator
  
  Parameters
  ----------
  X : object
      Always ignored, exists for compatibility.
  
  y : object
      Always ignored, exists for compatibility.
  
  groups : object
      Always ignored, exists for compatibility.
  
  Returns
  -------
  n_splits : int
      Returns the number of splitting iterations in the cross-validator.
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code: def get_n_splits(self, X=None, y=None, groups=None):
      """Returns the number of splitting iterations in the cross-validator
  
      Parameters
      ----------
      X : object
          Always ignored, exists for compatibility.
  
      y : object
          Always ignored, exists for compatibility.
  
      groups : object
          Always ignored, exists for compatibility.
  
      Returns
      -------
      n_splits : int
          Returns the number of splitting iterations in the cross-validator.
      """
      return self.n_splits
  
file_path: upgini/src/upgini/utils/blocked_time_series.py
imports: import numpy
  import numbers
  from sklearn.utils import indexable
  from sklearn.utils.validation import _num_samples
  from sklearn.model_selection import BaseCrossValidator
  from upgini.resource_bundle import bundle
label_desc: Use image node_id 2 for calling the BlockedTimeSeriesSplit obj's underlying member method code with example usage: obj.get_n_splits(X, y, groups) and returns: self